function exsvmroc2(data,margin,kppv,Cvec,kerneloptionvec);%%  code for big cross-validation procedure for%  SVM-ROC on real data.%% % 30/07/2004 A. Rakotomamonjy% clear all% close all% data='wpbc';% % kppv=5;% % Cvec=[0.1 1          10          ];% kerneloptionvec=[5 ];file=['../data/' data '/' data '.mat'];load(file);nbtrain=round(length(y)*0.90);classcode=[1 -1];margin=0.01;kernel='gaussian';span=1;lambda = 1e-12;  verbose=1;span=1;qpsize=500;NCV=10;for i=1:NCV    fprintf('\n%d..',i);    [xapp,yapp,xtest,ytest]=nfcv(x,y,NCV,i);    [xapp,xtest] = normalizemeanstd(xapp,xtest);    for j=1:length(Cvec)         C=Cvec(j);        for k=1:length(kerneloptionvec)            kerneloption=kerneloptionvec(k);                                    [xsup,w,w0,pos,timeps,alpha]=svmroc(xapp,yapp,C,kppv,margin,lambda,kernel,kerneloption,verbose,span,qpsize);            [AUC,tpr,fpr,w0]=svmroccurve(xapp,yapp,xsup,w,w0,kernel,kerneloption,span); % calcul de w0                        [AUCtest,tpr,fpr]=svmroccurve(xtest,ytest,xsup,w,w0,kernel,kerneloption,span);            [ypred]=svmrocval(xtest,xsup,w,w0,kernel,kerneloption,span);            [Conf,metric]=ConfusionMatrix(sign(ypred),ytest,classcode);            nbsv(j,k,i)=size(xsup,1);            MAUCtest(j,k,i)=AUCtest;            Maccur(j,k,i)=metric.accuracy;            Mprecision(j,k,i)=metric.precision;            Mfmeasure(j,k,i)=metric.fmeasure;            Mwracc(j,k,i)=metric.wracc;            Mdetection(j,k,i)=metric.detection;        end;    end;end;mauctest=mean(MAUCtest,3);maccur=mean(Maccur,3);mprecision=mean(Mprecision,3);mfmeasure=mean(Mfmeasure,3);mwracc=mean(Mwracc,3);mdetection=mean(Mdetection,3);sauctest=std(MAUCtest,0,3);saccur=std(Maccur,0,3);sprecision=std(Mprecision,0,3);sfmeasure=std(Mfmeasure,0,3);swracc=std(Mwracc,0,3);sdetection=std(Mdetection,0,3);save([data 'OptimMarginGaussian' int2str(margin*100) '-kppv' int2str(kppv)    '.mat']);